Low bit-rate coding of unvoiced segments of speech

ABSTRACT

A low-bit-rate coding technique for unvoiced segments of speech includes the steps of extracting high-time-resolution energy coefficients from a frame of speech, quantizing the energy coefficients, generating a high-time-resolution energy envelope from the quantized energy coefficients, and reconstituting a residue signal by shaping a randomly generated noise vector with quantized values of the energy envelope. The energy envelope may be generated with a linear interpolation technique. A post-processing measure may be obtained and compared with a predefined threshold to determine whether the coding algorithm is performing adequately.

CLAIM OF PRIORITY UNDER 35 U.S.C. §120

The present Application for Patent is a Continuation and claims priority to patent application Ser. No. 09/191,633 entitled “LOW BIT-RATE CODING OF UNVOICED SEGMENTS OF SPEECH,” filed Nov. 13, 1998, assigned to the assignee hereof and hereby expressly incorporated by reference herein.

BACKGROUND OF THE INVENTION

I. Field of the Invention

The present invention pertains generally to the field of speech processing, and more specifically to a method and apparatus for low bit-rate coding of unvoiced segments of speech.

II. Background

Transmission of voice by digital techniques has become widespread, particularly in long distance and digital radio telephone applications. This, in turn, has created interest in determining the least amount of information that can be sent over a channel while maintaining the perceived quality of the reconstructed speech. If speech is transmitted by simply sampling and digitizing, a data rate on the order of sixty-four kilobits per second (kbps) is required to achieve a speech quality of conventional analog telephone. However, through the use of speech analysis, followed by the appropriate coding, transmission, and resynthesis at the receiver, a significant reduction in the data rate can be achieved.

Devices that employ techniques to compress speech by extracting parameters that relate to a model of human speech generation are called speech coders. A speech coder divides the incoming speech signal into blocks of time, or analysis frames. Speech coders typically comprise an encoder and a decoder, or a codec. The encoder analyzes the incoming speech frame to extract certain relevant parameters, and then quantizes the parameters into binary representation, i.e., to a set of bits or a binary data packet. The data packets are transmitted over the communication channel to a receiver and a decoder. The decoder processes the data packets, unquantizes them to produce the parameters, and then resynthesizes the speech frames using the unquantized parameters.

The function of the speech coder is to compress the digitized speech signal into a low-bit-rate signal by removing all of the natural redundancies inherent in speech. The digital compression is achieved by representing the input speech frame with a set of parameters and employing quantization to represent the parameters with a set of bits. If the input speech frame has a number of bits N_(i) and the data packet produced by the speech coder has a number of bits N_(o), the compression factor achieved by the speech coder is C_(r)=N_(i)/N_(o). The challenge is to retain high voice quality of the decoded speech while achieving the target compression factor. The performance of a speech coder depends on (1) how well the speech model, or the combination of the analysis and synthesis process described above, performs, and (2) how well the parameter quantization process is performed at the target bit rate of N_(o) bits per frame. The goal of the speech model is thus to capture the essence of the speech signal, or the target voice quality, with a small set of parameters for each frame.

One effective technique to encode speech efficiently at low bit rate is multimode coding. A multimode coder applies different modes, or encoding-decoding algorithms, to different types of input speech frames. Each mode, or encoding-decoding process, is customized to represent a certain type of speech segment (i.e., voiced, unvoiced, or background noise) in the most efficient manner. An external mode decision mechanism examines the input speech frame and makes a decision regarding which mode to apply to the frame. Typically, the mode decision is done in an open-loop fashion by extracting a number of parameters out of the input frame and evaluating them to make a decision as to which mode to apply. Thus, the mode decision is made without knowing in advance the exact condition of the output speech, i.e., how similar the output speech will be to the input speech in terms of voice-quality or any other performance measure. An exemplary open-loop mode decision for a speech codec is described in U.S. Pat. No. 5,414,796, which is assigned to the assignee of the present invention and fully incorporated herein by reference.

Multimode coding can be fixed-rate, using the same number of bits N_(o) for each frame, or variable-rate, in which different bit rates are used for different modes. The goal in variable-rate coding is to use only the amount of bits needed to encode the codec parameters to a level adequate to obtain the target quality. As a result, the same target voice quality as that of a fixed-rate, higher-rate coder can be obtained at a significant lower average-rate using variable-bit-rate (VBR) techniques. An exemplary variable rate speech coder is described in U.S. Pat. No. 5,414,796, assigned to the assignee of the present invention and previously fully incorporated herein by reference.

There is presently a surge of research interest and strong commercial needs to develop a high-quality speech coder operating at medium to low bit rates (i.e., in the range of 2.4 to 4 kbps and below). The application areas include wireless telephony, satellite communications, Internet telephony, various multimedia and voice-streaming applications, voice mail, and other voice storage systems. The driving forces are the need for high capacity and the demand for robust performance under packet loss situations. Various recent speech coding standardization efforts are another direct driving force propelling research and development of low-rate speech coding algorithms. A low-rate speech coder creates more channels, or users, per allowable application bandwidth, and a low-rate speech coder coupled with an additional layer of suitable channel coding can fit the overall bit-budget of coder specifications and deliver a robust performance under channel error conditions.

Multimode VBR speech coding is therefore an effective mechanism to encode speech at low bit rate. Conventional multimode schemes require the design of efficient encoding schemes, or modes, for various segments of speech (e.g., unvoiced, voiced, transition) as well as a mode for background noise, or silence. The overall performance of the speech coder depends on how well each mode performs, and the average rate of the coder depends on the bit rates of the different modes for unvoiced, voiced, and other segments of speech. In order to achieve the target quality at a low average rate, it is necessary to design efficient, high-performance modes, some of which must work at low bit rates. Typically, voiced and unvoiced speech segments are captured at high bit rates, and background noise and silence segments are represented with modes working at a significantly lower rate. Thus, there is a need for a low-bit-rate coding technique that accurately captures unvoiced segments of speech while using a minimal number of bits per frame.

SUMMARY OF THE INVENTION

The present invention is directed to a low-bit-rate coding technique that accurately captures unvoiced segments of speech while using a minimal number of bits per frame. Accordingly, in one aspect of the invention, a method of coding unvoiced segments of speech advantageously includes the steps of extracting high-time-resolution energy coefficients from a frame of speech; quantizing the high-time-resolution energy coefficients; generating a high-time-resolution energy envelope from the quantized energy coefficients; and reconstituting a residue signal by shaping a randomly generated noise vector with quantized values of the energy envelope.

In another aspect of the invention, a speech coder for coding unvoiced segments of speech advantageously includes means for extracting high-time-resolution energy coefficients from a frame of speech; means for quantizing the high-time-resolution energy coefficients; means for generating a high-time-resolution energy envelope from the quantized energy coefficients; and means for reconstituting a residue signal by shaping a randomly generated noise vector with quantized values of the energy envelope.

In another aspect of the invention, a speech coder for coding unvoiced segments of speech advantageously includes a module configured to extract high-time-resolution energy coefficients from a frame of speech; a module configured to quantize the high-time-resolution energy coefficients; a module configured to generate a high-time-resolution energy envelope from the quantized energy coefficients; and a module configured to reconstitute a residue signal by shaping a randomly generated noise vector with quantized values of the energy envelope.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a communication channel terminated at each end by speech coders.

FIG. 2 is a block diagram of an encoder.

FIG. 3 is a block diagram of a decoder.

FIG. 4 is a flow chart illustrating the steps of a low-bit-rate coding technique for unvoiced segments of speech.

FIGS. 5A-5E are graphs of signal amplitude versus discrete time index.

FIG. 6 is a functional diagram depicting a pyramid vector quantization encoding process.

FIG. 7 is a functional diagram depicting a pyramid vector quantization decoding process.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In FIG. 1 a first encoder 10 receives digitized speech samples s(n) and encodes the samples s(n) for transmission on a transmission medium 12, or communication channel 12, to a first decoder 14. The decoder 14 decodes the encoded speech samples and synthesizes an output speech signal s_(SYNTH)(n). For transmission in the opposite direction, a second encoder 16 encodes digitized speech samples s(n), which are transmitted on a communication channel 18. A second decoder 20 receives and decodes the encoded speech samples, generating a synthesized output speech signal s_(SYNTH)(n).

The speech samples s(n) represent speech signals that have been digitized and quantized in accordance with any of various methods known in the art including, e.g., pulse code modulation (PCM), companded μ-law, or A-law. As known in the art, the speech samples s(n) are organized into frames of input data wherein each frame comprises a predetermined number of digitized speech samples s(n). In an exemplary embodiment, a sampling rate of 8 kHz is employed, with each 20 ms frame comprising 160 samples. In the embodiments described below, the rate of data transmission may advantageously be varied on a frame-to-frame basis from 8 kbps (full rate) to 4 kbps (half rate) to 2 kbps (quarter rate) to 1 kbps (eighth rate). Varying the data transmission rate is advantageous because lower bit rates may be selectively employed for frames containing relatively less speech information. As understood by those skilled in the art, other sampling rates, frame sizes, and data transmission rates may be used.

The first encoder 10 and the second decoder 20 together comprise a first speech coder, or speech codec. Similarly, the second encoder 16 and the first decoder 14 together comprise a second speech coder. It is understood by those of skill in the art that speech coders may be implemented with a digital signal processor (DSP), an application-specific integrated circuit (ASIC), discrete gate logic, firmware, or any conventional programmable software module and a microprocessor. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. Alternatively, any conventional processor, controller, or state machine could be substituted for the microprocessor. Exemplary ASICs designed specifically for speech coding are described in U.S. Pat. No. 5,727,123, assigned to the assignee of the present invention and fully incorporated herein by reference, and U.S. Pat. No. 5,784,532, entitled “VOCODER ASIC,” issued Jul. 21, 1998, assigned to the assignee of the present invention, and fully incorporated herein by reference.

In FIG. 2, an encoder 100 that may be used in a speech coder includes a mode decision module 102, a pitch estimation module 104, an LP analysis module 106, an LP analysis filter 108, an LP quantization module 110, and a residue quantization module 112. Input speech frames s(n) are provided to the mode decision module 102, the pitch estimation module 104, the LP analysis module 106, and the LP analysis filter 108. The mode decision module 102 produces a mode index I_(M) and a mode M based upon the periodicity of each input speech frame s(n). Various methods of classifying speech frames according to periodicity are described in U.S. Pat. No. 5,911,128, entitled “METHOD AND APPARATUS FOR PERFORMING REDUCED RATE VARIABLE RATE VOCODING,” issued Jun. 8, 1999, assigned to the assignee of the present invention, and fully incorporated herein by reference. Such methods are also incorporated into the Telecommunication Industry Association Industry Interim Standards TIA/EIA IS-127 and TIA/EIA IS-733.

The pitch estimation module 104 produces a pitch index I_(P) and a lag value P₀ based upon each input speech frame s(n). The LP analysis module 106 performs linear predictive analysis on each input speech frame s(n) to generate an LP parameter a. The LP parameter a is provided to the LP quantization module 110. The LP quantization module 110 also receives the mode M. The LP quantization module 110 produces an LP index I_(LP) and a quantized LP parameter {circumflex over (α)}. The LP analysis filter 108 receives the quantized LP parameter {circumflex over (α)} in addition to the input speech frame s(n). The LP analysis filter 108 generates an LP residue signal R[n], which represents the error between the input speech frames s(n) and the quantized linear predicted parameters {circumflex over (α)}. The LP residue R[n], the mode M, and the quantized LP parameter {circumflex over (α)} are provided to the residue quantization module 112. Based upon these values, the residue quantization module 112 produces a residue index IR and a quantized residue signal {circumflex over (R)}[n].

In FIG. 3 a decoder 200 that may be used in a speech coder includes an LP parameter decoding module 202, a residue decoding module 204, a mode decoding module 206, and an LP synthesis filter 208. The mode decoding module 206 receives and decodes a mode index I_(M), generating therefrom a mode M. The LP parameter decoding module 202 receives the mode M and an LP index I_(LP). The LP parameter decoding module 202 decodes the received values to produce a quantized LP parameter {circumflex over (α)}. The residue decoding module 204 receives a residue index I_(R), a pitch index I_(P), and the mode index I_(M). The residue decoding module 204 decodes the received values to generate a quantized residue signal {circumflex over (R)}[n]. The quantized residue signal {circumflex over (R)}[n] and the quantized LP parameter {circumflex over (α)} are provided to the LP synthesis filter 208, which synthesizes a decoded output speech signal ŝ[n] therefrom.

Operation and implementation of the various modules of the encoder 100 of FIG. 2 and the decoder of FIG. 3 are known in the art, and are described in detail in L. B. Rabiner & R. W. Schafer Digital Processing of Speech Signals 396-453 (1978), which is fully incorporated herein by reference. An exemplary encoder and an exemplary decoder are described in U.S. Pat. No. 5,414,796, previously fully incorporated herein by reference.

The flow chart of FIG. 4 illustrates a low-bit-rate coding technique for unvoiced segments of speech in accordance with one embodiment. The low-rate unvoiced coding mode shown in the embodiment of FIG. 4 advantageously offers multimode speech coders a lower average bit rate while preserving an overall high voice quality by capturing unvoiced segments accurately with a low number of bits per frame.

In step 300 the coder performs an external rate decision, identifying incoming speech frames as either unvoiced or not unvoiced. The rate decision is done by considering a number of parameters extracted from the speech frame S[n], where n=1, 2, 3, . . . , N, such as the energy of the frame (E), the frame periodicity (Rp), and the spectral tilt (Ts). The parameters are compared with a set of predefined thresholds. A decision is made as to whether the current frame is unvoiced based upon the results of the comparisons. If the current frame is unvoiced, it is encoded as an unvoiced frame, as described below.

The frame energy may advantageously be determined in accordance with the following equation: $E = {\frac{1}{N}*{\sum\limits_{m = 1}^{N}{{S\lbrack m\rbrack}*{S\lbrack m\rbrack}}}}$

The frame periodicity may advantageously be determined in accordance with the following equation:

Rp=max-over-all-k {(S[n], S[n+k])}, for k=1,2, . . . , N,

where (x[n], x[n+k]) is an autocorrelation function of x. The spectral tilt may advantageously be determined in accordance with the following equation:

Ts=(Eh/El),

where Eh and El are the energy values of Sl[n] and Sh[n], Sl and Sh being the low-pass and high-pass components of the original speech frame S[n], which components may advantageously be generated by a set of low-pass and high-pass filters.

In step 302 LP analyses is conducted to create the linear predictive residue of the unvoiced frame. The linear predictive (LP) analysis is accomplished with techniques that are known in the art, as described in the aforementioned U.S. Pat. No. 5,414,796 and L. B. Rabiner & R. W. Schafer Digital Processing of Speech Signals 396-458 (1978), both previously fully incorporated herein by reference. The N-sample, unvoiced LP residue, R[n], where n=1, 2, . . . , N, is created from the input speech frame S[n], where n=1, 2, . . . , N. The LP parameters are quantized in the line spectral pair (LSP) domain with known LSP quantization techniques, as described in either of the above-listed references. A graph of original speech signal amplitude versus discrete time index is illustrated in FIG. 5A. A graph of quantized unvoiced speech signal amplitude versus discrete time index is illustrated in FIG. 5B. A graph of original unvoiced residue signal amplitude versus discrete time index is illustrated in FIG. 5C. A graph of energy envelope amplitude versus discrete time index is illustrated in FIG. 5D. A graph of quantized unvoiced residue signal amplitude versus discrete time index is illustrated in FIG. 5E.

In step 304 fine-time resolution energy parameters of the unvoiced residue are extracted. A number (M) of local energy parameters E_(i), where i=1, 2, . . . , M, is extracted from the unvoiced residue R[n] by performing the following steps. The N-sample residue R[n] is divided into (M−2) sub-blocks X_(i), where i=2, 3, . . . , M−1, with each block X₁ having a length of L=N/(M−2). The L-sample past residue block X₁ is obtained from the past quantized residue of the previous frame. (The L-sample past residue block X₁ incorporates the last L samples of the N-sample residue of the last speech frame.) The L-sample future residue block X_(M) is obtained from the LP residue of the following frame. (The L-sample future residue block X_(M) incorporates the first L samples of the N-sample LP residue of the next speech frame.) A number M of local energy parameters E_(i), where i=1, 2, . . . , M, is created from each of the M blocks X_(i), where i=1, 2, . . . , M, in accordance with the following equation: $E_{i} = {\frac{1}{L}*{\sum\limits_{m = 1}^{L}{{X_{i}\lbrack m\rbrack}*{X_{i}\lbrack m\rbrack}}}}$

In step 306 the M energy parameters are encoded with Nr bits according to a pyramid vector quantization (PVQ) method. Thus, the M−1 local energy values E_(i), where i=2, 3, . . . , M, are encoded with Nr bits to form quantized energy values W_(i), where i=2, 3, . . . , M. A K-step PVQ encoding scheme with bits N₁, N₂, . . . , N_(K) is employed such that N₁+N₂+ . . . +N_(K)=Nr, the total number of bits available for quantizing the unvoiced residue R[n]. For each of k-stages, where k=1, 2, . . . , K, the following steps are performed. For the first stage (i.e., k=1), the band number is set to B_(k)=B₁=1, and the band length is set to L_(k)=1. For each band B_(k), the mean value mean_(j), where j=1, 2, . . . , B_(k), in accordance with the following equation: ${mean}_{j} = {\frac{1}{L_{j}}*{\sum\limits_{m = 1}^{L_{j}}E_{m}}}$

The B_(k) mean values mean_(j), where j=1, 2, . . . , B_(k), are quantized with N_(k)=N₁ bits to form the quantized set of mean values qmean_(j), where j=1, 2, . . . , B_(k). The energy belonging to each band B_(k) is divided by the associated quantized mean value qmean_(j), generating a new set of energy values {E_(k,i)}={E_(1,i)}, where i=1, 2, . . . , M. In the first-stage case (i.e., for k=1) for each i, where i=1, 2, 3, . . . , M,:

E _(1,i) =E _(i)/qmean₁

The process of breaking into sub-bands, extracting the means for each band, quantizing the means with bits available for the stage, and then dividing the components of the sub-band by the quantized mean of the subband is repeated for each subsequent stage k, where k=2, 3, . . . , K−1.

In the K-th stage, the sub-vectors of each of the B_(K) sub-bands are quantized with individual VQs designed for each band, using a total of N_(K) bits. The PVQ encoding process for M=8 and stage=4 is illustrated by way of example in FIG. 6.

In step 308 M quantized energy vectors are formed. The M quantized energy vectors are formed from the codebooks and the Nr bits representing the PVQ information by reversing the above-described PVQ encoding process with the final residue sub-vectors and quantized means. The PVQ decoding process for M=3 and stage k=3 is illustrated by way of example in FIG. 7. As those skilled in the art would understand, the unvoiced (UV) gains may be quantized with any conventional encoding technique. The encoding scheme need not be restricted to the PVQ scheme of the embodiment described in connection with FIGS. 4-7.

In step 310 a high-resolution energy envelope is formed. An N-sample (i.e., the length of the speech frame), high-time-resolution energy envelope ENV[n], where n=1, 2, 3, . . . , N, is formed from the decoded energy values W_(i), where i=1, 2, 3, . . . , M, in accordance with the computations described below. The M energy values represent the energies of M−2 sub-frames of the current residue of speech, each sub-frame having a length L=N/M. The values W₁ and W_(M) represent the energy of the past L samples of the last frame of residue and the energy of the future L samples of the next frame of residue, respectively.

If W_(m−1), W_(m), and W_(m+1), are representative of the energies of the (m−1)th, m-th, and (m+1)-th sub-band, respectively, then the samples of the energy envelope ENV[n], for n=m*L−L/2 to n=m*L+L/2, representing the m-th sub-frame are computed as follows: For n=m*L−L/2, until n=m*L,

ENV[n]={square root over (W_(m−1))}+(1/L)*(n−m*L+L)*({square root over (W _(m))}−{square root over (W _(m−1))}

And for n=m*L, until n=m*L+L/2,

ENV[n]={square root over (W_(m))}(1/L)*(n−m*L)*({square root over (W _(m+1))}−{square root over (W_(m))}).

The steps for computing the energy envelope ENV[n] are repeated for each of the M−1 bands, letting m=2, 3, 4, . . . , M, to compute the entire energy envelope ENV[n], where n=1, 2, . . . , N, for the current residue frame.

In step 312 a quantized unvoiced residue is formed by coloring random noise with the energy envelope ENV[n]. The quantized unvoiced residue qR[n] is formed in accordance with the following equation:

qR[n]=Noise[n]*ENV[n], for n=1, 2, . . . , N,

where Noise[n] is a random white noise signal with unit variance, which is advantageously artificially generated by a random number generator in sync with the encoder and the decoder.

In step 314 a quantized unvoiced speech frame is formed. The quantized unvoiced residue qS[n] is generated by inverse-LP filtering of the quantized unvoiced speech with conventional LP synthesis techniques, as known in the art and described in the aforementioned U.S. Pat. No. 5,414,796 and L. B. Rabiner & R. W. Schafer Digital Processing of Speech Signals 396-458 (1978), both previously fully incorporated herein by reference.

In one embodiment a quality-control step can be performed by measuring a perceptual error measure such as, e.g., perceptual signal-to-noise ratio (PSNR), which is defined as: ${PSNR} = {10*\log \quad 10\frac{\sum\limits_{n = 1}^{N}\left( {{x\lbrack n\rbrack} - {e\lbrack n\rbrack}} \right)^{2}}{\sum\limits_{n = 1}^{N}{{e\lbrack n\rbrack}*{e\lbrack n\rbrack}}}}$

where x[n]=h[n]*R[n], and e(n)=h[n]*qR[n], with “*” denoting a convolution or filtering operation, h(n) being a perceptually weighted LP filter, and R[n] and qR[n] being, respectively, the original and quantized unvoiced residue. The PSNR is compared with a predetermined threshold. If the PSNR is less than the threshold, the unvoiced encoding scheme did not perform adequately and a higher-rate encoding mode may be applied instead to more accurately capture the current frame. On the other hand, if the PSNR exceeds the predefined threshold, the unvoiced encoding scheme has performed well and the mode-decision is retained.

Preferred embodiments of the present invention have thus been shown and described. It would be apparent to one of ordinary skill in the art, however, that numerous alterations may be made to the embodiments herein disclosed without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited except in accordance with the following claims. 

What is claimed is:
 1. A method for low bit rate speech coding of unvoiced speech, comprising; identifying an incoming speech frame as an unvoiced speech frame; performing linear predictive analysis on the unvoiced speech frame to create an unvoiced liner predictive residue; extracting high-time-resolution energy parameters from the unvoiced linear predictive residue, wherein extracting high-time-resolution energy parameters comprises extracting a number (M) of local energy parameters E_(i), where i=1,2, . . . , M, is extracted from an unvoiced residue R[n] by performing the following steps; dividing N-sample residue R[n] into (M−2) sub-blocks X_(i), where i=2,3, . . . , M−1, with each block X_(i) having a length of L=N/(M−2); obtaining an L-sample past residue block X₁ from a past quantized residue of a previous frame; obtaining an L-sample future residue block X_(M) from the linear predictive residue of a following frame; and creating a number M of local energy parameters where E_(i), where i=1,2, . . . , M, from each of the M blocks X_(i), where i=1,2, . . . , M, in accordance with the following equation; ${E_{i} = {\frac{1}{L}*{\sum\limits_{m = 1}^{L}{{X_{i}\lbrack m\rbrack}*{X_{i}\lbrack m\rbrack}}}}};$

encoding the high-time-resolution energy parameters; quantizing the high-time-resolution energy parameters to form quantized energy vectors; forming a high-time-resolution energy envelope; generating a quantized unvoiced residue by coloring random noise with the high-time-resolution energy envelope; and generating a quantized unvoiced speech frame.
 2. The method of claim 1 wherein the forming a high-time-resolution energy envelope comprises using look ahead parameter values from a next frame and previous parameter values from a preceding frame to smooth the energy envelope for a current frame at the frame boundaries.
 3. The method of claim 1 wherein the encoding the high-time-resolution energy parameters comprises encoding the energy parameters according to a pyramid vector quantization method.
 4. A method for low bit rate speech coding of unvoiced speech, comprising; identifying an incoming speech frame as an unvoiced speech frame; performing linear predictive analysis on the unvoiced speech frame to create an unvoiced linear predictive residue; extracting high-time-resolution energy parameters from the unvoiced linear predictive residue; encoding the high-time-resolution energy parameters; quantizing the high-time-resolution energy parameters to form quantized energy vectors; forming a high-time-resolution energy envelope; generating a quantized unvoiced residue by coloring random noise with the high-time-resolution energy envelope; and generating a quantized unvoiced speech frame, wherein the forming a high resolution energy envelope comprises forming an N-sample high-time-resolution energy envelope ENV[n], the length of a speech frame, where n=1,2,3, . . . , N from decoded energy values W_(i), where i=1,2,3, . . . , M, in accordance with the following computations where: M energy values represent the energies of M−2 sub-frames of a current residue of speech, each sub-frame having a length L=N/M; values W_(i) aud W_(M) represent the energy of the past L samples of the last frame of residue and the energy of the future L samples of the next frame of residue, respectively; and W_(m−1), W_(m), and W_(m+1), are representative of the energies of the (m−1)th, m-th, and (m+1)-th sub-band, respectively; samples of the energy envelope ENV[n], for n=m*L−L/2 to n=m*L+L/2, representing the m-th sub-frame are computed as: ENV[n]={square root over (W_(m−1))}+(1/L)*(n−m*L+L)*({square root over (W _(m))}−{square root over (W_(m−1))}), for n=m*L−L/2, until n=m*L; and ENV[n]={square root over (W_(m))}+(1/L)*(n−m*L)*({square root over (W _(m+1))}−{square root over (W_(m))}), for n=m*L, until n=m*L+L/2, wherein the steps for computing the energy envelope ENV[n] are repeated for each of the M−1 bands, letting m=2,3,4, . . . , M, to compute the entire energy envelope ENV[n], where n=1,2, . . . , N, for a current residue frame.
 5. A speech coder for low bit rate speech coding of unvoiced speech, comprising; means for identifying an incoming speech frame as an unvoiced speech frame; means for performing linear predictive analysis on the unvoiced speech frame to create an unvoiced linear predictive residue; means for extracting high-time-resolution energy parameters from the unvoiced linear predictive residue, by extracting a number (M) of local energy parameters E_(i), where i=1,2, . . . , M, is extracted from an unvoiced residue R[n] by performing the following steps: dividing N-sample residue R[n] (M−2) sub-blocks X_(i), where i=2,3, . . . , M−1, with each block X_(i) having a length of L=N/(M−2); obtaining an L-sample past residue block X₁ from a past quantized residue of a previous frame; obtaining an L-sample future residue block X_(M) from the linear predictive residue of a following frame; and creating a number M of local energy parameters E_(i), where i=1,2, . . . , M, from each of the M blocks X_(i), where i=1,2, . . . , M, in accordance with the following equation: ${E_{i} = {\frac{1}{L}*{\sum\limits_{m = 1}^{L}{{X_{i}\lbrack m\rbrack}*{X_{i}\lbrack m\rbrack}}}}};$

means for encoding the high-time-resolution energy parameters; means for quantizing the high-time-resolution energy parameters to form quantized energy vectors; means for forming a high-time-resolution energy envelope; means for generating a quantized unvoiced residue by coloring random noise with the high-time-resolution energy envelope; and means for generating a quantized unvoiced speech frame. 